Background and Aim It has been well documented that Helicobacter pylori (H. pylori) infection is a risk factor for aggravating gastric mucosal atrophy. However, the exact molecular mechanism mediating this process is not fully elucidated. The purpose of this study was to identify biomarkers, which may predict the risk for progression of chronic atrophic gastritis (CAG) with H. pylori. Methods GSE27411 was downloaded from the Gene Expression Omnibus. The differentially expressed genes (DEGs) between H. pylori‐infected samples without CAG and H. pylori‐infected CAG samples were analyzed. Gene Ontology and pathway enrichment analyses were performed, followed by protein–protein interaction network construction. We used immunohistochemistry analysis to identify DEGs in 20 chronic gastritis, 20 CAG, and 22 gastric cancer (GC) specimens. Results A total of 303 upregulated and 26 downregulated DEGs were identified. The pathways enriched by upregulated DEGs were mainly related to fat digestion and absorption, peroxisome proliferator‐activated receptor signaling pathway, and chemical carcinogenesis. Cytochrome P450, family 3, subfamily A, polypeptide 4 (CYP3A4) had the highest degrees in protein–protein interaction network. Moreover, the positive rates of CYP3A4 protein expression in chronic gastritis, CAG, and GC were 10% (2/20), 55% (11/20), and 77.3% (17/22), respectively (P < 0.001). The Kaplan–Meier analysis revealed that elevated expression of CYP3A4 was significantly associated with worse overall survival and first progression, respectively (P < 0.0001). Conclusion According to the findings of this study, the expression of CYP3A4 might be related to the potential carcinogenic transformation of CAG to GC. Therefore, CYP3A4 may be biomarkers to predict progression of CAG and poor prognosis of gastric cancer.

Hepatocellular carcinoma (HCC) is an aggressive cancer type with poor prognosis; thus, there is especially necessary and urgent to screen potential prognostic biomarkers for early diagnosis and novel therapeutic targets. In this study, we downloaded target data sets from the GEO database, and obtained codifferentially expressed genes using the limma R package and identified key genes through the protein–protein interaction network and molecular modules, and performed GO and KEGG pathway analyses for key genes via the clusterProfiler package and further determined their correlations with clinicopathological features using the Oncomine database. Survival analysis was completed in the GEPIA and the Kaplan–Meier plotter database. Finally, correlations between key genes, cell types infiltrated in the tumor microenvironment (TME), and hypoxic signatures were explored based on the TIMER database. From the results, 11 key genes related to the cell cycle were determined, and high levels of these key genes’ expression were focused on advanced and higher grade status HCC patients, as well as in samples of TP53 mutation and vascular invasion. Besides, the 11 key genes were significantly associated with poor prognosis of HCC and also were positively related to the infiltration level of MDSCs in the TME and the HIF1A and VEGFA of hypoxic signatures, but a negative correlation was found with endothelial cells (ECs) and hematopoietic stem cells. The result determined that 11 key genes (RRM2, NDC80, ECT2, CCNB1, ASPM, CDK1, PRC1, KIF20A, DTL, TOP2A, and PBK) could play a vital role in the pathogenesis of HCC, drive the communication between tumor cells and the TME, and act as probably promising diagnostic, therapeutic, and prognostic biomarkers in HCC patients.

To investigate the role of LAMC1 in gastric cancer (GC), if it is of great importance to identify tumour driver genes with prognostic value. Patients and Methods: GC-related gene expression profile data were downloaded from TCGA. R-limma package and univariate Cox regression were used to identify the differentially expressed genes (DEGs) and survival-genes, respectively. Then, the ClusterProfiler package was used to analyse the Gene Ontology and pathway enrichment of DEGs. Cytoscape was used to build a protein interaction network (PPI) and identify key genes. The GEPIA2 and TIMER databases were used to validate the differential expression of LAMC1. The relationship between LAMC1 and the prognosis of GC was analysed by the KM. GSEA and GSVA were used to analyse the major activated and mutated pathways, respectively. Real-time fluorescence quantitative PCR (RT-qPCR) was used to reidentify the expression of LAMC1 in GES-1 and 5 GC cell lines. Finally, we explored the relationship between LAMC1 and FGFR1. Results: A total of 266 DEGs were be selected, which were mainly enriched in extracellular structure organization. LAMC1 was identified as one of the hub genes. The expression of LAMC1 was significantly higher in GC tissue than in paracancerous tissues, and the prognosis of the GC patient with high expression of LAMC1 was relatively poor. Univariate and multivariate Cox analysis indicated that LAMC1 could be used as an independent prognostic indicator. The results of GSEA and GSVA showed that LAMC1 was mainly enriched in pathways such as MYOGENESIS and UV_RESPONSE_DN. The RT-qPCR results showed that the expression level in AGS cells was significantly higher than that in gastric epithelial cells. LAMC1 may play a role in the development of gastric cancer by influencing FGFR1. Conclusion: LAMC1 may mediate the occurrence and development of GC and has potential as a biomarker for the prognosis and treatment of GC.

Non-cardioembolic ischemic stroke (IS) is the predominant subtype of IS. This study aimed to construct a nomogram for recurrence risks in patients with non-cardioembolic IS in order to maximize clinical benefits. From April 2015 to December 2019, data from consecutive patients who were diagnosed with non-cardioembolic IS were collected from Lanzhou University Second Hospital. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. Multivariable Cox regression analyses were used to identify the independent risk factors. A nomogram model was constructed using the “rms” package in R software via multifactor Cox regression. The accuracy of the model was evaluated using the receiver operating characteristic (ROC), calibration curve, and decision curve analyses (DCA). A total of 729 non-cardioembolic IS patients were enrolled, including 498 (68.3%) male patients and 231 (31.7%) female patients. Among them, there were 137 patients (18.8%) with recurrence. The patients were randomly divided into training and testing sets. The Kaplan–Meier survival analysis of the training and testing sets consistently revealed that the recurrence rates in the high-risk group were significantly higher than those in the low-risk group (p < 0.01). Moreover, the receiver operating characteristic curve analysis of the risk score demonstrated that the area under the curve was 0.778 and 0.760 in the training and testing sets, respectively. The nomogram comprised independent risk factors, including age, diabetes, platelet–lymphocyte ratio, leukoencephalopathy, neutrophil, monocytes, total protein, platelet, albumin, indirect bilirubin, and high-density lipoprotein. The C-index of the nomogram was 0.752 (95% CI: 0.705~0.799) in the training set and 0.749 (95% CI: 0.663~0.835) in the testing set. The nomogram model can be used as an effective tool for carrying out individualized recurrence predictions for non-cardioembolic IS.

BackgroundSkin cutaneous melanoma (SKCM) is the deadliest type of cutaneous malignancy. Ubiquitination is a process of protein sorting and degradation that exhibits multiple functions in the progression of various tumors. This study aimed to characterize a set of genes for ubiquitination in SKCM.MethodsThe expression patterns of ubiquitin-associated genes (URGs) and the corresponding clinical information in SKCM tissues were comprehensively analyzed based on The Cancer Genome Atlas (TCGA) database. We performed univariate and multivariate Cox proportional regression models to characterize the risk scores and identify four critical genes related to prognostic ubiquitination (HCLS1, CORO1A, NCF1 and CCRL2), which were used to construct the prognostic signatures. We also studied the effects of HCLS1, CORO1A and CCRL2 on tumor metastasis-related indicators at the cellular level through in vitro experiments.ResultsSKCM patients in the low-risk group showing a longer survival than those in the high-risk group. Characteristic risk scores correlated with several clinicopathological variables and reflected the infiltration of multiple immune cells. In addition, the knockdown of CLS1, CORO1A and CCRL2 affected cellular malignant biological behavior through the EMT signaling pathway.ConclusionThis study provides a novel and prospective strategy to improve the clinical survival of SKCM patients.